PLEASE SCROLL DOWN FOR ARTICLE
This article was downloaded by: [National Taiwan University]On: 30 August 2008
Access details: Access Details: [subscription number 788856278] Publisher Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Applied Economics Letters
Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713684190
Impact of foreign-listed single stock futures on the domestic underlying stock
markets
M. -W. Hung a; C. -F. Lee b; L. -C. So a
a College of Management, National Taiwan University, No.1, Section 4, Roosevelt Road, Taipei, Taiwan. b Department of Finance, School of Business, Rutgers University, New Brunswick, New Jersey, USA. Online Publication Date: 15 July 2003
To cite this Article Hung, M. -W., Lee, C. -F. and So, L. -C.(2003)'Impact of foreign-listed single stock futures on the domestic underlying stock markets',Applied Economics Letters,10:9,567 — 574
To link to this Article: DOI: 10.1080/1350485032000100206 URL: http://dx.doi.org/10.1080/1350485032000100206
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Impact of foreign-listed single stock futures
on the domestic underlying stock markets
M . - W . H U N G * , C . - F . L E Ey and L . -C . SO
College of Management, National Taiwan University, No.1, Section 4, Roosevelt Road, Taipei, Taiwan; yDepartment of Finance, School of Business, Rutgers University, New Brunswick, New Jersey, USA
The purpose of this article is to investigate whether foreign-listed single stock futures (SSFs) would have any impact on their domestic underlying stock markets. GARCH (1,1) and GJR-GARCH (1,1) are used to analyse the data from the London International Financial Future and Options Exchange (LIFFE) in this research. Results show that the introduction of the foreign listed SSF contracts seems to have more explanatory power with respect to the higher volatility of their domestic spot markets than the announcement of the SSF contracts. Also, for two of the nine securities, the daily activity shocks of the foreign-listed SSFs are responsible for the higher conditional volatility of their home underlying stocks, while the activity that is forecastable but highly variable across days diminishes the conditional volatility of the underlying stocks.
I . I N T R O D U C T I O N
Since the trading of futures and other derivatives has become more frequent in recent years, a growing number of studies have been done to examine the influence of deriv-atives trading on the underlying stock markets.
However, intensive debates have been conducted with respect to this issue in the financial literature. One category of the literature has the perception that derivatives trading does result in higher underlying stock market volatility (see, for example, Conrad, 1989; Harris, 1989; Damodaran, 1990; Harris et al., 1994; Antoniou et al., 1998). Gulen and Mayhew (2000) found that stock index futures trading was responsible for an increase in conditional volatility in the spot markets of the USA and Japan. By showing a change in the asymmetric volatility response, McKenzie et al. (2000) suggested that the introduction of individual share futures (ISFs) in the Sydney Futures Exchange in Australia had an impact on the underlying security market. Rahman (2001) also reported that volatility in the 30 stocks included in the DJIA could have been increased by the introduction of futures and futures options on the DJIA.
Another category of the literature argues that the introduction of derivatives lowers (or has no significant
effect on) the volatility of the underlying stock markets (see, for instance, Ma and Rao, 1988; Edward, 1988; Choi and Subrahmanyam, 1994).
Many studies have been done on the effect of derivatives trading on the underlying spot markets. Some of them focused on the impact of stock index futures trading on the underlying asset markets (Gulen and Mayhew, 2000; Rahman, 2001). However, many macroeconomic factors other than the introduction of stock index futures may have affected the market indices. Investigating only changes in the market index volatility with the introduction of futures may be inadequate to conclude that the introduction of stock index futures influences the spot markets. With the goal to avoid this potential inference problem in mind, it may be more appropriate to use the data of single stock future (SSF) contracts to examine this controversial issue. As SSF contracts were designed for investors to manage firm-specific risk, the underlying stock markets could be more sensitive to SSF contracts. Hence, the results of this kind of study may be more reliable.
Although SSFs or ISFs have had leading roles in some studies (for example, McKenzie et al., 2000), most of the studies have focused on examining the impact of the domestic listed SSFs on their underlying stock markets.
Applied Economics LettersISSN 1350–4851 print/ISSN 1466–4291 online # 2003 Taylor & Francis Ltd http://www.tandf.co.uk/journals
DOI: 10.1080/1350485032000100206
Applied Economics Letters, 2003, 10, 567–574
567 * Corresponding author. E-mail: hung@mba.ntu.edu.tw
As the internationalization of worldwide financial markets becomes ever more rapid, firms have increasingly chosen to list their securities in foreign countries. Following this trend, numerous studies have been devoted to the effect of foreign listing. Among them, the event study is the most prevalent method used to detect whether foreign-listed securities would influence the returns of their domestic stocks. Worldwide evidence has shown that the cumulated abnormal returns of the domestic firms are significantly influenced by their stocks that were listed in foreign exchanges after overseas listing (see, for example, Foerster and Karolyi, 1993, 1996; Damodaran et al., 1993).
While much work has been done on the effects of foreign listed stocks on their domestic stock markets, there has been little attention given to the impact of foreign listed derivatives on their domestic underlying markets. Therefore, the purpose of this article is to determine whether the introduction of foreign-listed SSFs would affect their home underlying stock markets.
The plan of this article is as follows: the data used here are described in Section II; the methodology employed is described in Section III; the empirical results are presented in Section IV, and the conclusions of the article are presented in the final section.
I I . D A T A
The data used in this article are obtained from the London International Financial Future and Options Exchange (LIFFE) database. Currently, LIFFE has single stock future contracts (SSF) traded on 118 individual stocks in England, the USA, and Europe. More than 80% of the SSFs listed on the LIFFE are traded on securities outside England, and these SSFs are so-called ‘foreign-listed’ for their domestic stock markets. The SSFs listed on the LIFFE are also called universal stock futures (USFs). Each SSF represents 100 shares of the underlying stock in Europe (except for Italy and England), or 1000 shares of the underlying stock in Italy, the USA, and England. The contracts have delivery dates of two consecutive months or two near quarter months. The contracts are settled in cash. In addition, there are no specific daily price movement limits or position limits.
The data used in this research consist of the top ten active single stock future contracts (SSF) listed on the LIFFE. However, among these SSFs, the underlying stock of the second active one (i.e. Vodafone Group plc) is listed on the London Stock Exchange. Hence, based on our criterion of foreign listing, the data of this SSF is left out. Table 1 lists the dates of announcement and introduction of the remaining nine SSF contracts.
I I I . M E T H O D O L O G Y
The daily returns of the underlying stock (rt) are computed
by lnð pt= pt1Þ, where ptand pt1are close prices at time t
and time t 1.
To determine if the underlying stock returns are influ-enced by the announcement or the introduction of the SSF contracts, the samples were divided into two groups: a pre-announcement group (a pre-introduction group) and a post-announcement group (a post-introduction group) were compared. As listed in Table 2, visual examination identified that the variances of the underlying stock returns have no significant differences between the pre- and post-announcement (introduction) groups.
However, when ACF and PACF graphs of the series rt and r2t were plotted it was found that autocorrelation
of rt was very low, while autocorrelation of r2t is quite
high. (To save space, the ACF and PACF graphs are omitted here.) This implied that even though one could claim that rtwas serial uncorrelated, one could not jump
to the conclusion that rt was serial independent. Hence,
volatility models were employed to capture such depen-dence in the series rt.
Bollerslev (1986) proposed a useful extension of ARCH, known as the generalized ARCH (GARCH) model to avoid estimating too many parameters in the ARCH model. Akgiray (1989) reported that compared with various ARCH models, GARCH (1,1) performed best on estimating conditional volatility. Hence, one began with assuming that the conditional variance of daily returns of the underlying stocks has the form of GARCH (1,1). The GARCH (1,1) model used in the research is as follows: ht ¼ þ r2t1þht1 ð1Þ
where ht and ht1 are the current and lagged values of
conditional variance of the underlying stock daily returns and r2t1 is the lagged value of the squared return.
After estimating the parameters in Equation 1 for the returns of pre-announcement (pre-introduction) period and the post-announcement (post-introduction) period, one adopted the statistic suggested by Harnett and Soni (1991) to test if there would be significant differences in the estimated parameters for the pre and post periods. Rahman (2001) employed this statistic and concluded that the conditional volatility in DJIA spot market exhib-ited no structure changes caused by the introduction of index future or futures options. The statistic is described as follows:
t ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðxxprexxpostÞ ðMpreMpostÞ ðnpre1Þ_2preþ ðnpre1Þ_2pre
. ðnpreþnpost2Þ 1=npreþ1=npost v u u t ð2Þ
568
M.-W. Hung
et al.
Table 1. The dates of announcement and introduction for the nine SSF contracts Name
(Symbol) Country
Listing
exchange Data period
Total observations Announcement data Observations pre-announcement Observations post-announcement Introduction data Observations pre-introduction Observations post-introduction
Eni SpA (ENI) Italy Borsa
Italianaa 28/11/1995– 19/4/2002 1573 13/12/2000 1242 331 29/01/2001 1270 303 Telecom Italia SpA (TI) Italy Borsa Italiana 02/01/1995– 19/4/2002 1793 13/12/2000 1462 331 29/01/2001 1490 303 Banco Bilbao Vizcaya Argentaria SA (BVA) Spain Bolsa De Madridb 02/01/1995– 19/4/2002 1764 02/05/2001 1527 237 14/05/2001 1535 229 Telecom Italia Mobile SpA (TIM)
Italy Borsa
Italiana
17/07/1995– 19/4/2002
1664 12/03/2001 1391 273 19/03/2001 1396 268
Nokia OYJ (NOK) Finland Helsinki
Exchangec
02/01/1995– 19/4/2002
1772 20/09/2000 1389 383 29/01/2001 1475 297
Enel SpA (ENL) Italy Borsa
Italianad
02/11/1999– 19/4/2002
610 12/03/2001 337 273 19/03/2001 342 268
UniCredito Italiano SpA (UC)
Italy Borsa Italiana 02/01/1995– 19/4/2002 1796 12/03/2001 1523 273 19/03/2001 1528 268 Telefonica SA (TEF) Spain Bolsa De Madride 02/01/1995– 19/4/2002 1764 13/12/2000 1437 327 29/01/2001 1465 299 Royal Dutch Petroleum Co (RD) Netherlands Euronext Amsterdamf 02/01/1995– 19/4/2002 1806 20/09/2000 1414 392 29/01/2001 1502 304
Notes:aENI is also listed on the New York Stock Exchange (NYSE). bBVA is also listed on the NYSE.
cNOK is also listed on the NYSE and the Stockholm Stock Exchange. dENL is also listed on the NYSE.
eTEF is also listed on the NYSE, the Buenos Aires Stock Exchange, the Lima Stock Exchange, the Sao Paulo Stock Exchange, the London Stock Exchange, the Paris Stock Exchange, the Frankfurt Stock Exchange, and the Tokyo Stock Exchange.
fRD is also listed on the NYSE.
Table 2. Mean and variance for the subset of all samples
Name (Symbol)
Pre-announcement period Post-announcement period Pre-introduction period Post-introduction period
Mean Variance Mean Variance Mean Variance Mean Variance
Eni SpA (ENI) 0.000691 0.000319 0.000853 0.00036 0.000727 0.000318 0.00072 0.000365
Telecom Italia SpA (TI) 0.001223 0.000496 0.00108 0.000437 0.001215 0.000494 0.00125 0.000439
Banco Bilbao Vizcaya Argentaria SA (BVA) 0.001299 0.000442 0.00057 0.000521 0.001303 0.0004404 0.000632 0.0005838
Telecom Italia Mobile SpA (TIM) 0.001349 0.000567 0.00102 0.000513 0.001336 0.000567 0.00099 0.000512
Nokia OYJ (NOK) 0.002375 0.001006 0.00248 0.002057 0.002082 0.001072 0.00243 0.002039
Enel SpA (ENL) 0.00039 0.000282 0.00029 0.000318 0.00072 0.000286 0.000135 0.000313
UniCredito Italiano SpA (UC) 0.001032 0.000556 0.000219 0.000313 0.000988 0.000556 0.000457 0.00031
Telefonica SA (TEF) 0.001365 0.000445 0.00117 0.000673 0.001361 0.000456 0.00139 0.000645
Royal Dutch Petroleum Co (RD) 0.000868 0.000262 0.00045 0.00034 0.000732 0.000263 0.00016 0.000359
where xxpreðxxpostÞ, _2preð_2postÞ, and npre(npre) are the sample
mean, sample variance, and sample size of , , or ( þ ) for all 10 stocks for the pre and (post) period, re-spectively; Mpre and Mpost are the respective population
means. The statistic follows a t distribution with a (npreþnpost2) degree of freedom.
In order to examine more precisely the influence of the introduction of SSFs on the spot markets, one investigated whether there is a relationship between the conditional volatility of the underlying stock and the magnitude of the SSF trading activity. Another model suggested by Bessembinder and Seguin (1992) was employed and data after the introduction of SSF contracts were used. In Bessembinder and Seguin’s opinion, trade volume and open interest are two important trading activity variables, influencing the conditional volatility. They suggested that the trading activity variables should be detrended at first, and then the detrended series should be divided into expected and unexpected components using ARIMA specifications. According to them, ‘The unexpected com-ponent of the detrended series is interpreted as the daily activity shock. The expected component of the detrended series . . . reflects activity that is forecastable but highly variable across days.’ They reported that the expected com-ponents of trade volume and open interest had negative effects on the conditional volatility, while the unexpected components had opposite effects. Following Gulen and Mayhew (2000), after applying the Augmented Dicky– Filler test (ADF) to check if the trading activity variables
have a unit root, we used models smaller than ARIMA (5,1,5) to partition the series with a unit root into two parts, and used models smaller than ARMA (5,5) to divide the series without a unit root into two parts. Then the additional four explanatory variables – the unexpected trading volume, the expected trading volume, the unex-pected open interest, and exunex-pected open interest – were added to the GJR-GARCH (1,1) model as follows:
rt¼c þ "t ð3Þ
ht¼0þ1"2t1þ2It1"2t1þ3ht1þ
4Unexp Vol þ 5ExpVol þ 6Unexp OI þ 7ExpOI
ð4Þ where Equation 3 is the mean equation of the underlying stock returns (rt); the dummy variable It1in Equation 4
takes the value of one when "t1 is negative, otherwise it
takes the value of zero, reflecting the asymmetry effects of bad and good news on the conditional volatility in the GJR-GARCH model (i.e. leverage effect).
I V . E M P I R I C A L R E S U L T S
The estimated coefficients on r2t1and ht1(i.e. and ) in
Equation 1 for the nine stocks for the pre-announcement and announcement period (pre-introduction and post-introduction period) are reported in Table 3 (Table 4). The report of is omitted due to its exceedingly small value and relative insignificance.
Impact of foreign-listed futures on domestic markets
571
Table 3. Estimates for the announcement case from the following variance equation ht¼ þ r2t1þht1
Name (Symbol)
Pre-announcement period Post-announcement period
Eni SpA (ENI) 0.113112* 0.796738* 0.067301 0.795375*
(0.0000) (0.0000) (0.1758) (0.0000)
Telecom Italia SpA (TI) 0.080625* 0.885708* 0.101047* 0.836687*
(0.0000) (0.0000) (0.0137) (0.0000)
Banco Bilbao Vizcaya Argentaria SA (BVA) 0.136621* 0.851847* 0.134303* 0.836446*
(0.0000) (0.0000) (0.0369) (0.0000)
Telecom Italia Mobile SpA (TIM) 0.066357* 0.908380* 0.076868 0.858846*
(0.0000) (0.0000) (0.0566) (0.0000)
Nokia OYJ (NOK) 0.096286* 0.879968* 0.017888* 0.927999*
(0.0000) (0.0000) (0.0329) (0.0000)
Enel SpA (ENL) 0.144801* 0.827821* 0.129729* 0.788963*
(0.0001) (0.0000) (0.0052) (0.0000)
UniCredito Italiano SpA (UC) 0.134052* 0.783408* 0.113359* 0.849718*
(0.0000) (0.0000) (0.0047) (0.0000)
Telefonica SA (TEF) 0.088051* 0.908845* 0.080419* 0.822002*
(0.0000) (0.0000) (0.0464) (0.0000)
Royal Dutch Petroleum Co (RD) 0.081990* 0.904634* 0.138284* 0.791281*
(0.0000) (0.0000) (0.0003) (0.0000)
Notes: 1. Figures in parentheses are p-values
2. Figures marked with* are statistically significant at the 5% level.
The statistics for and both for the announcement case and the introduction case are computed. Table 5 displays the results. From Table 5, it is concluded that within the research samples, is significantly different between the pre- and post-introduction periods rather than between the pre- and post-announcement periods, and that is not significantly different in either the announcement or the introduction cases. That is, the volatility of the spot market changes may be due to the introduction of the SSF, while the announcement of introduction may have fewer effects.
In order to examine the influence of SSF introduction on the spot markets more accurately, we introduce Equations 3 and 4 into the methodology. Because we are interested in the relationship between the conditional volatility of the underlying stock and the magnitude of the SSF trading activity, we just report the estimates of the Equation 4 in Table 6. As shown in Table 6, the coefficient (2) on the
dummy variable It1 in Equation 4 is positive in eight of
the nine securities and statistically significant in three, reflecting that bad shocks indeed impact conditional volatility more than good news in our researched data. In addition, as one focuses on the influence of SSF trading activity, one finds that the estimated parameters in Eni SpA and UniCredito Italiano SpA have the same sign suggested by Bessembinder and Seguin (i.e. 4and 6should be
posi-tive; 5and 7should be negative). Of the nine securities,
the coefficient (4) on the unexpected trading volume is
positive in six, although it is statistically significant only in Enel SpA. As for the coefficient (5) on the expected
trading volume, it is slightly negative in all nine but not
statistically significant. Four of the nine securities have a positive coefficient (6) on the unexpected open interest,
but this was only statistically significant in Eni SpA. Of the nine securities, the coefficient (7) on the unexpected
trading volume is slightly negative in three, but insignifi-cant in all nine.
The significance of the coefficients for the explanatory variables depends on the security. For example, the unex-pected open interest has a significantly positive effect on the conditional volatility in Eni SpA. As for Enel SpA, the unexpected trading volume has a significantly positive effect on the conditional volatility.
V . C O N C L U S I O N
This article used data obtained from the London International Financial Future and Options Exchange (LIFFE) database to determine whether the introduction of SSF contracts listed on the LIFFE would influence the volatility of their domestic underlying stock returns. It began by employing the GARCH (1,1) model suggested by Akgiray (1989) and the statistic suggested by Harnett
572
M.-W. Hung
et al.
Table 4. Estimates for the introduction case from the following variance equation ht¼ þ r2t1þht1
Name (Symbol)
Pre-introduction period Post-introduction period
Eni SpA (ENI) 0.110516* 0.799542* 0.072073 0.774387*
(0.0000) (0.0000) (0.1551) (0.0000)
Telecom Italia SpA (TI) 0.077432* 0.890021* 0.113404* 0.819254*
(0.0000) (0.0000) (0.0292) (0.0000)
Banco Bilbao Vizcaya Argentaria SA (BVA) 0.140819* 0.845117* 0.116841* 0.853460*
(0.0000) (0.0000) (0.0341) (0.0000)
Telecom Italia Mobile SpA (TIM) 0.066208* 0.908498* 0.077092 0.849148*
(0.0000) (0.0000) (0.0666) (0.0000)
Nokia OYJ (NOK) 0.098373* 0.883250* 0.003237 0.640056
(0.0000) (0.0000) (0.9329) (0.8712)
Enel SpA (ENL) 0.178085* 0.794890* 0.125322* 0.788202*
(0.0001) (0.0000) (0.0039) (0.0000)
UniCredito Italiano SpA (UC) 0.133847* 0.782976* 0.102581* 0.869125*
(0.0000) (0.0000) (0.0055) (0.0000)
Telefonica SA (TEF) 0.085773* 0.909836* 0.138526 0.680863*
(0.0000) (0.0000) (0.0698) (0.0000)
Royal Dutch Petroleum Co (RD) 0.036586* 0.960101* 0.170286* 0.777228*
(0.0000) (0.0000) (0.0008) (0.0000)
Notes: 1. Figures in parentheses are p-values
2. Figures marked with* are statistically significant at the 5% level.
Table 5. The statistic t for b and g
Bases
Announcement date 0.700682 1.22830263
Issuing date 0.075101 2.41919083*
Note: An asterisk marks statistical significance at the 5% level.
and Soni (1991) to determine if there would be a change in the volatility of underlying stock returns before and after the announcement (introduction) of the SSF contracts. It concluded that within the research samples, the intro-duction of the foreign listed SSF contracts induced structure changes in the conditional variances of their domestic spot markets, while the announcement of the SSF contracts had no significant effect.
In order to examine more accurately the influence of SSF contracts on the spot markets after their introduction, it then included four additional variables – the unexpected trading volume, the expected trading volume, the unex-pected open interest, and exunex-pected open interest – into the GJR-GARCH (1,1) model suggested by Gulen and Mayhew (2000). The findings support the leverage effect. In addition, as one considered the effects of the SSF trading activity, it appears that the daily activity shocks of the foreign listed futures raise the conditional volatility of their home underlying stocks, while the activity that is forecastable but highly variable across days lessens the conditional volatility of the underlying stocks.
Based on the samples of the SSF contracts and their underlying stock returns, the findings provide evidence that the foreign-listed derivatives trading has an effect on the volatility of their home underlying spot markets. Moreover, the results suggest that even though the SSFs are designed for investors to easily and cheaply hedge firm-specific risk of stocks, the high conditional volatility
of the underlying stock returns arising from SSFs trading in the highly interactive foreign financial markets may be a dissatisfactory by-product.
R E F E R E N C E S
Akgiray, V. (1989) Conditional heteroskedasticity in time series of stock returns: evidence and forecasts, Journal of Business, 62, 55–80.
Antoniou, A., Holmes, P. and Priestley, R. (1998) The effects of stock index futures trading on stock index volatility: an analysis of the asymmetric response of volatility to news, Journal of Futures Markets, 18, 151–66.
Bessembinder, H. and Seguin, P. (1992) Futures-trading activity and stock price volatility, Journal of Finance, 47, 2015–34. Bollerslev, T. (1986) Generalized autoregressive conditional
heteroskedasticity, Journal of Econometrics, 31, 307–27. Choi, H. and Subrahmanyam, A. (1994) Using intraday data to
test for effects of index futures on the underlying markets, Journal of Futures Markets, 14, 293–322.
Conrad, J. (1989) The price effect of option introduction, Journal of Finance, 44, 487–98.
Damodaran, A. (1990) Index futures and stock market volatility, Review of Futures Markets, 9, 442–57.
Damodaran, A., Crocker, L. and Van Harlow, W. (1993) The effects of international dual listings on stock price behavior, New York University Salomon Brothers Working Paper S-93-41.
Impact of foreign-listed futures on domestic markets
573
Table 6. Estimates from the following variance equation:
ht¼0þ1"2t1þ2It1"t12 þ3ht1þ4Unexp Vol þ 5ExpVol þ 6Unexp OI þ 7ExpOI
Name (Symbol) Vol / OI 0 1 2 3 4 5 6 7
Eni SpA (ENI) 103/105 0.000197 0.081417 0.018770 0.779947* 6.94E-05 0.000262 0.000178* 1.72E-05 (0.73430) (0.2105) (0.8039) (0.0000) (0.0811) (0.8159) (0.0420) (0.4440) Telecom Italia SpA (TI) 7.27E-05 0.053221 0.162719* 0.966161* 2.17E-05 0.000256 3.95E-05 2.83E-07 103/104 (0.6913) (0.0525) (0.0000) (0.0000) (0.7514) (0.7080) (0.5116) (0.9290) Banco Bilbao Vizcaya 1.52E-05 0.058679 0.190039* 0.952755* 3.57E-06 2.98E-05 8.88E-05 2.34E-05 Argentaria SA (BVA) 103/104 (0.2774) (0.3517) (0.0268) (0.0000) (0.9348) (0.2339) (0.5612) (0.3908)
Telecom Italia Mobile SpA 0.000238 0.025802 0.029424 0.776000* 4.23E-05 0.000576 0.00026* 4.63E-05 (TIM) 103/104 (0.9497) (0.7040) (0.6943) (0.0000) (0.2177) (0.9640) (0.0000) (0.1430) Nokia OYJ (NOK) 103/104 0.000447 0.084967 0.074332 0.885431* 1.79E-05 0.000245 0.000600 4.89E-05
(0.1516) (0.0835) (0.2003) (0.0000) (0.7597) (0.3721) (0.1928) (0.4595) Enel SpA (ENL) 103/104 2.13E-05* 0.11948* 0.224035* 0.926538* 5.42E-05* 1.13E-05 0.00013* 3.69E-06
(0.0002) (0.0006) (0.0000) (0.0000) (0.0029) (0.6810) (0.0263) (0.7640) UniCredito Italiano SpA (UC) 0.000305 0.180868* 0.043454 0.695399* 2.23E-05 0.002220 0.000119 4.94E-05 103/104 (0.9893) (0.0354) (0.7123) (0.0000) (0.4654) (0.9907) (0.4971) (0.2201) Telefonica SA (TEF) 103/104 0.000131 0.066938 0.156840 0.65705* 4.48E-05 8.73E-05 0.000362 3.77E-05
(0.1007) (0.4393) (0.2128) (0.0001) (0.5383) (0.6974) (0.2629) (0.6729) Royal Dutch Petroleum Co 3.17E-05* 0.129717 0.131113 0.70574* 0.000122 0.000123 5.83E-05* 1.71E-05 (RD) 103/103 (0.0006) (0.0878) (0.2341) (0.0000) (0.2872) (0.3412) (0.0000) (0.2298)
Notes: 1. Figures in parentheses are p-values.
2. An asterisk marks statistical significance at the 5% level.
3. For computational reasons, we standardized the trading volume and open interest series to be of a mean less than one, following Gulen and Mayhew (2000). Scaling units are listed under the security name.
Edward, F. (1988) Does futures trading increase stock market volatility, Financial Analysts Journal, 44, 63–69.
Foerster, S. R. and Karolyi, G. A. (1993) International listing of stocks: the case of Canada and the US, Journal of International Business Studies, 24, 763–84.
Foerster, S. R. and Karolyi, G. A. (1996) The effects of market segmentation and illiquidity on asset prices: evidence from foreign stocks listing in the US, Working Paper No. 96-6, Fisher College of Business, Ohio State University. Foerster, S. R. and Karolyi, G. A. (1999) The effects of market
segmentation and investor recognition on asset prices: evidence from foreign stocks listing in the United States, Journal of Finance, 54, 981–1013.
Gulen, H. and Mayhew, S. (2000) Stock index futures trading and volatility in international equity markets, Journal of Futures Markets, 20, 661–85.
Harnett, D. and Soni, A. (1991) Statistical Methods for Business and Economics, 4th edn, Addison-Wesley, Reading, MA.
Harris, L. (1989) S&P 500 cash stock price volatilities, Journal of Finance, 44, 1155–76.
Harris, L., Sofianos, G. and Shapiro, J. (1994) Programming trading and intraday volatility, Review of Financial Studies, 7, 653–85.
Licht, A. (1998) Regulatory arbitrage for real: international securities regulation in a world of interacting securities markets, Virginia Journal of International Law, 38.
Ma, C. and Rao, R. (1988) Information asymmetry and option trading, Financial Review, 23, 39–51.
McKenzie, M. D., Brailsford, T. J. and Faff, R. W. (2000) New insights into the impact of the introduction of futures trading on stock price volatility, Journal of Futures Markets, 21, 237–55.
Rahman, S. (2001) The introduction of derivatives on the Dow Jones Industrial Average and their impact on the volatility of component stocks, Journal of Futures Markets, 21, 633–53.
